FSQC Protocol
Saashi Bedford
Abstract
This protocol is a guide to conducting a visual quality control (QC) of Freesurfer outputs. Quality control is important to make sure that the cortical surface reconstruction and grey and white matter classification are accurate.
To examine outputs, ten images per subject are generated (see https://github.com/sbedford0/Generate_FSQC_images), https://github.com/sbedford0/Generate_FSQC_images), showing the cortical segmentation and surface boundaries at different slices and views of the brain (3 axial; 3 coronal; 4 sagittal). The pial surface is delineated in red, and the white matter surface in blue. These images should be examined to check that the reconstructed surfaces follow the cortical boundaries accurately.
Steps
Instructions
Download, save and unzip the Image Rating QC app from https://github.com/sbedford0/imageratingQCApp
Follow the instructions in the “readme” file to run the program:
- The first time you run the program, in a terminal, run ‘ npm install ’ in the app directory
- Run ‘ npm run build ’ to build the app for production in the build folder and create a local version (you only need to do this once)
- Once you have done this, any time you want to start the app, run ‘ npm run start ’ in the app directory to launch the app in a web browser
- Click ‘browse’ to select and load in your images and start rating! Note : if you have a large dataset, try to limit yourself to maximum ~2000 subjects in one session to avoid fatigue and distraction
Images will be displayed one by one
Rate by either clicking the buttons or using arrow keys
- Thumbs down (← key): BAD
- Shrug (↓ key): MINOR ERROR
- Thumbs up (→ key): GOOD
- Brain with ripples (↑ key): VISIBLE MOTION / POOR SCAN QUALITY
See Guidelines for rating criteria, and Materials for examples of outputs and ratings
When you have gone through all of the images, a download button will appear. Click this to download your ratings as a csv file.
After downloading your ratings, convert scores to numeric
- GOOD = 1
- MINOR ERROR = 2
- MOTION = 3
- BAD = 4
Calculate an average to generate a score between 1-4 for each subject
Ideally have (at least) one other rater score your images
- Compare scores (average score per participant), and take an average and/or resolve any major discrepancies between scores
- Check correlation and/or ICC between raters to make sure inter-rater reliability is reasonable
Apply your exclusion criteria to exclude scans with a score below a certain threshold or cut off, or use these scores as a covariate in your analysis
